Data Points Calculator

Autodesk Tandem

Data Points Calculator

Add one row per stream group. Select a frequency and retention profile – totals update automatically.

Example: “1 min + 3 years” in one row, “15 min + 6 months” in another.

Data points by stream, frequency, and retention
Streams Frequency Retention Data Points Actions
Total 0

Examples

These real-world examples show how frequency and retention choices affect total Data Points. Use them as reference patterns when grouping similar streams in the calculator above.

High Frequency Example

1 min – 6 months

Manufacturing quality + throughput monitoring on an assembly line. A Tandem digital twin is used to correlate machine behavior with production outcomes using time-series telemetry. High-frequency readings support anomaly detection (spikes, drifts) and root-cause analysis across shifts.

  • Streams (example groups): 120 total (machines + stations)
  • Typical signals: cycle time, motor current, vibration RMS, oven temperature, reject counts
  • Pattern: readings every 1 min retained for 180 days for trending + investigations
  • Why it’s high: minute-level data enables fast detection and production correlation

Medium Frequency Example

15 min – 2 years

Commercial building energy optimization for an office tower. Tandem aggregates BAS and submeter data to track energy performance, verify savings, and support monthly reporting. A 15-minute cadence is common for energy analytics and aligns well with demand and utility intervals.

  • Streams (example groups): 600 (electric submeter circuits, AHU temps, zone averages)
  • Typical signals: kW/kWh, chilled water supply/return, outside air temp, AHU discharge temp
  • Pattern: readings every 15 min retained for 730 days for year-over-year baselines
  • Why it’s medium: frequent enough for operational insights without “process telemetry” volume

Low Frequency Example

1 hour – 5 years

District utility + environmental compliance reporting for a campus. Hourly snapshots support long-range trending, ESG reporting, and historical comparisons while keeping storage and compute predictable. This is ideal when the use-case is reporting and benchmarking, not real-time operations.

  • Streams (example groups): 250 (steam, chilled water, gas, water, weather station)
  • Typical signals: totalized flow, hourly demand, temperature/humidity, boiler efficiency KPIs
  • Pattern: readings every 1 hour retained for 1825 days for multi-year trends
  • Why it’s low: cadence fits reporting needs; minimizes Data Points footprint